Post-transcriptional Regulation Coordinating Transcription and Translation During Circadian Oscillation and Stress Recovery in Plants
Bibliographic record
Abstract
ABSTRACT Circadian rhythms orchestrate gene expression to align plant growth and development with daily environmental cycles. However, the post-transcriptional mechanisms that coordinate transcriptional and translational rhythmicity remain incompletely understood. To address this, we analyzed time-series transcriptome and translatome profiles in Arabidopsis seedlings, identifying 5,185 genes with rhythmicity at both levels. These genes were classified into four distinct groups based on phase and amplitude differences between transcription and translation. Circadian mRNAs with high oscillation amplitudes tended to undergo co-translational RNA decay (CTRD), whereas intronless genes displayed the lowest amplitudes, likely due to their mRNA instability and short half-lives. While CTRD and NAD⁺ capping modulate amplitude differences, intronless and circadian translational efficiency (TE) influence both phase and amplitude variations. Additionally, CTRD, NAD + capping and circadian TE facilitate fast recovery of heat-induced genes to normal hemostasias. Collectively, our findings demonstrate that these post-transcriptional regulation shapes both synchronized and decoupled transcription and translation during plants response to diel and environmental dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".